Industrial processes are commonly controlled by computer-based systems such as Distributed Control Systems (DCS) or Programmable Logic Controllers (PLC). These systems receive input from instruments that measure process variables, such as temperatures, pressures, flow rates, and the like. Some systems use a layer of control known as Basic Regulatory Control (BRC), in which algorithms use the received inputs to calculate how to manipulate a single process actuator for the purpose of controlling a process variable. The industry standard Proportional-Integral-Derivative (PID) algorithm is commonly applied at this level. A typical PID controller generally has a desired setpoint for a process variable and one output to an actuator or to another PID controller.
Advanced Process Control (APC) technology provides greater capability compared to the basic functionality of PID controllers. Two broad classes of APC technology are independently applied in industrial processes: Model Predictive Control (MPC) and rule-based control. Although MPC can handle large systems through scalable algorithms, it does not perform well when its models cannot accurately predict variable trajectories in all operating regions. A major weakness of rule-based approaches is that a human expert often cannot describe a process as precisely as a mathematical model, which leads to non-optimal process operation by giving the process too much cushion. Furthermore, a large rule-based system that must consider many inputs and move many outputs can become unwieldy with thousands of rules that must be written to address process interactions. In other words, the two classes of APC technology are utilized independently of each other, requiring process operators to choose only one technology and its respective weaknesses.
In addition, human operators cannot monitor and adjust control parameters as frequently and accurately as APC technologies, which results in great difficulty operating processes at their optimum operation points. While operators can decide to push processes and process equipment to their limits, such an approach is inefficient and leads to equipment or process breakdowns. Such downtime leads to further costs and lost profits. On the other hand, operators can keep processes well within their limits and operate with a cushion but this can also lead to lost profits. Therefore, operators are left to choose between two non-optimal approaches.